An incremental adaptive neural network model for online noisy data regression and its application to compartment fire studies

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)827-836
Journal / PublicationApplied Soft Computing Journal
Issue number1
Publication statusPublished - Jan 2011


This paper presents a probabilistic-entropy-based neural network (PENN) model for tackling online data regression problems. The network learns online with an incremental growth network structure and performs regression in a noisy environment. The training samples presented to the model are clustered into hyperellipsoidal Gaussian kernels in the joint space of the input and output domains by using the principles of Bayesian classification and minimization of entropy. The joint probability distribution is established by applying the Parzen density estimator to the kernels. The prediction is carried out by evaluating the expected conditional mean of the output space with the given input vector. The PENN model is demonstrated to be able to remove symmetrically distributed noise embedded in the training samples. The performance of the model was evaluated by three benchmarking problems with noisy data (i.e., Ozone, Friedman#1, and Santa Fe Series E). The results show that the PENN model is able to outperform, statistically, other artificial neural network models. The PENN model is also applied to solve a fire safety engineering problem. It has been adopted to predict the height of the thermal interface which is one of the indicators of fire safety level of the fire compartment. The data samples are collected from a real experiment and are noisy in nature. The results show the superior performance of the PENN model working in a noisy environment, and the results are found to be acceptable according to industrial requirements. © 2010 Elsevier B.V. All rights reserved.

Research Area(s)

  • Artificial neural network, Compartment fire, Kernel regression